as seen in the work [1]}, [2]}, [3]}, [4]}. Increasing \(b_s\)  generally leads to an increase in the model error rate. How the increase in \(b_s\)  changes the generalization dynamics of trained DNNs has remained a challenging research question with different directions of investigation. Most of the works that have attempted to unravel the origin of generalization approached it from perspectives that decoupled model optimization and generalization performance. In contrast to earlier works, the problem of generalization gap is investigated simultaneously from both optimization and generalization perspectives.
